Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery

Abstract

Intraoperative hypotension (IOH) is common during major surgery and is associated with a poor postoperative outcome. Hypotension Prediction Index (HPI) is an algorithm derived from machine learning that uses the arterial waveform to predict IOH. The aim of this study was to assess the diagnostic ability of HPI working with non-invasive ClearSight system in predicting impending hypotension in patients undergoing major gynaecologic oncologic surgery (GOS). In this retrospective analysis hemodynamic data were downloaded from an Edwards Lifesciences HemoSphere platform and analysed. Receiver operating characteristic curves were constructed to evaluate the performance of HPI working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure < 65 mmHg for > 1 min. Sensitivity, specificity, positive predictive value and negative predictive value were computed at a cutpoint (the value which minimizes the difference between sensitivity and specificity). Thirty-one patients undergoing GOS were included in the analysis, 28 of which had complete data set. The HPI predicted hypotensive events with a sensitivity of 0.85 [95% confidence interval (CI) 0.73–0.94] and specificity of 0.85 (95% CI 0.74–0.95) 15 min before the event [area under the curve (AUC) 0.95 (95% CI 0.89–0.99)]; with a sensitivity of 0.82 (95% CI 0.71–0.92) and specificity of 0.83 (95% CI 0.71–0.93) 10 min before the event [AUC 0.9 (95% CI 0.83–0.97)]; and with a sensitivity of 0.86 (95% CI 0.78–0.93) and specificity 0.86 (95% CI 0.77–0.94) 5 min before the event [AUC 0.93 (95% CI 0.89–0.97)]. HPI provides accurate and continuous prediction of impending IOH before its occurrence in patients undergoing GOS in general anesthesia.

Performance of the Hypotension Prediction Index May Be Overestimated Due to Selection Bias

“…We are aware of eight subsequent validation studies of Hypotension Prediction Index. 3,[5][6][7][8][9][10][11] In these studies, the Hypotension Prediction Index was either downloaded directly from a HemoSphere or EV1000 monitor (Edwards Lifesciences, USA), or calculated post hoc from the arterial waveform using the same algorithm. Only one of these studies compares the predictive performance of the Hypotension Prediction Index to that of the concurrent MAP value.…”

“…Three studies used the same data selection as the index publication and presented similarly skewed receiver operating characteristics curves with high specificity 5–7 (Wijnberge et al 7 also did the “forward” analysis described below and is counted there as well). Another three showed a “skewed” receiver operating characteristics curve with a very high specificity, but did not specify exactly how nonhypotensive events were selected.…”

The Use of Non-Invasive Continuous Blood Pressure Measuring (ClearSight®) during Central Neuraxial Anaesthesia for Caesarean Section—A Retrospective Validation Study

“…In addition to monitoring real-time blood pressure, ClearSight ® offers the ability to predict hypotension based on a machine-learning index—the so-called “Hypotension Prediction Index” (HPI). Frassanito et al determined a sensitivity and also a specificity of 0.85 for ClearSight ® by correctly predicting hypotension 15 min before the event under general anaesthesia in major surgical procedures [ 23 ]. Focusing on awake caesarean sections, Frassanito et al demonstrated a sensitivity and specificity of 0.97 in HPI prediction accuracy 2 min before the onset of hypotension [ 24 ].…”

Performance of the Hypotension Prediction Index With Noninvasive Arterial Pressure Waveforms in Awake Cesarean Delivery Patients Under Spinal Anesthesia

“…There are studies investigating its application in nonpregnant surgical populations with noninvasive waveforms which report that it correlates well. [4][5][6] To the authors knowledge, this is the first study to test the HPI on awake spontaneously ventilating pregnant patients.…”